Standard
Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images. / Camarasa, Robin; Faure, Alexis; Crozier, Thomas; Bos, Daniel; de Bruijne, Marleen.
Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. ed. / Esther Puyol Anton; Mihaela Pop; Maxime Sermesant; Victor Campello; Alain Lalande; Karim Lekadir; Avan Suinesiaputra; Oscar Camara; Alistair Young. Springer, 2021. p. 385-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12592 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Camarasa, R, Faure, A, Crozier, T, Bos, D
& de Bruijne, M 2021,
Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images. in E Puyol Anton, M Pop, M Sermesant, V Campello, A Lalande, K Lekadir, A Suinesiaputra, O Camara & A Young (eds),
Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12592 LNCS, pp. 385-391, 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020, Lima, Peru,
04/10/2020.
https://doi.org/10.1007/978-3-030-68107-4_40APA
Camarasa, R., Faure, A., Crozier, T., Bos, D.
, & de Bruijne, M. (2021).
Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images. In E. Puyol Anton, M. Pop, M. Sermesant, V. Campello, A. Lalande, K. Lekadir, A. Suinesiaputra, O. Camara, & A. Young (Eds.),
Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers (pp. 385-391). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12592 LNCS
https://doi.org/10.1007/978-3-030-68107-4_40Vancouver
Camarasa R, Faure A, Crozier T, Bos D
, de Bruijne M.
Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images. In Puyol Anton E, Pop M, Sermesant M, Campello V, Lalande A, Lekadir K, Suinesiaputra A, Camara O, Young A, editors, Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Springer. 2021. p. 385-391. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12592 LNCS).
https://doi.org/10.1007/978-3-030-68107-4_40Author
Camarasa, Robin ; Faure, Alexis ; Crozier, Thomas ; Bos, Daniel ; de Bruijne, Marleen. / Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images. Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. editor / Esther Puyol Anton ; Mihaela Pop ; Maxime Sermesant ; Victor Campello ; Alain Lalande ; Karim Lekadir ; Avan Suinesiaputra ; Oscar Camara ; Alistair Young. Springer, 2021. pp. 385-391 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12592 LNCS).
Bibtex
@inproceedings{2e58bd3faf244c7cbad5d5e8848401f8,
title = "Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images",
abstract = "Every segmentation task is uncertain due to image resolution, artefacts, annotation protocol etc. Propagating those uncertainties in a segmentation pipeline can improve the segmentation. This article aims to assess if segmentation can benefit from uncertainty of an auxiliary unsupervised task - the reconstruction of the input image. This auxillary task could help the network focus on rare examples that are otherwise poorly segmented. The method was applied to segmentation of myocardial infarction areas on cardiac magnetic resonance images.",
author = "Robin Camarasa and Alexis Faure and Thomas Crozier and Daniel Bos and {de Bruijne}, Marleen",
year = "2021",
doi = "10.1007/978-3-030-68107-4_40",
language = "English",
isbn = "9783030681067",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "385--391",
editor = "{Puyol Anton}, Esther and Mihaela Pop and Maxime Sermesant and Victor Campello and Alain Lalande and Karim Lekadir and Avan Suinesiaputra and Oscar Camara and Alistair Young",
booktitle = "Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers",
address = "Switzerland",
note = "11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020 ; Conference date: 04-10-2020 Through 04-10-2020",
}
RIS
TY - GEN
T1 - Uncertainty-Based Segmentation of Myocardial Infarction Areas on Cardiac MR Images
AU - Camarasa, Robin
AU - Faure, Alexis
AU - Crozier, Thomas
AU - Bos, Daniel
AU - de Bruijne, Marleen
PY - 2021
Y1 - 2021
N2 - Every segmentation task is uncertain due to image resolution, artefacts, annotation protocol etc. Propagating those uncertainties in a segmentation pipeline can improve the segmentation. This article aims to assess if segmentation can benefit from uncertainty of an auxiliary unsupervised task - the reconstruction of the input image. This auxillary task could help the network focus on rare examples that are otherwise poorly segmented. The method was applied to segmentation of myocardial infarction areas on cardiac magnetic resonance images.
AB - Every segmentation task is uncertain due to image resolution, artefacts, annotation protocol etc. Propagating those uncertainties in a segmentation pipeline can improve the segmentation. This article aims to assess if segmentation can benefit from uncertainty of an auxiliary unsupervised task - the reconstruction of the input image. This auxillary task could help the network focus on rare examples that are otherwise poorly segmented. The method was applied to segmentation of myocardial infarction areas on cardiac magnetic resonance images.
U2 - 10.1007/978-3-030-68107-4_40
DO - 10.1007/978-3-030-68107-4_40
M3 - Article in proceedings
AN - SCOPUS:85101507101
SN - 9783030681067
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 385
EP - 391
BT - Statistical Atlases and Computational Models of the Heart. MandMs and EMIDEC Challenges - 11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
A2 - Puyol Anton, Esther
A2 - Pop, Mihaela
A2 - Sermesant, Maxime
A2 - Campello, Victor
A2 - Lalande, Alain
A2 - Lekadir, Karim
A2 - Suinesiaputra, Avan
A2 - Camara, Oscar
A2 - Young, Alistair
PB - Springer
T2 - 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020
Y2 - 4 October 2020 through 4 October 2020
ER -
